Performing a familiar task, just using the built-in packages of Maxima.

According to an earlier posting, I had suggested a recipe for ‘Perpendicularizing’ a matrix, that was to represent a Quadric Equation, according to methods which I learned in “Linear 1″. That approach used the application ‘wxMaxima’, which is actually a fancy front-end for the application ‘Maxima’. But the main drawback with the direct approach I had suggested, was that it depended on the package ‘lapack’, which I had written, takes a long time to compile.

Since writing that posting, I discovered that some users cannot even get ‘lapack’ to compile, making that a broken, unusable package for them. Yet, the desire could still exist, to carry out the same project. Therefore, I have now expounded on this, by using the package ‘eigen’, which is built in to Maxima, and which should work for more users, assuming there is no bug in the way Maxima was built.

The following work-sheet explains what initially goes wrong when using the package ‘eigen’, and, how to remedy the initial problem…

Work-Sheet Formatted as a Letter-Sized PDF

Work-Sheet in EPUB3 for Phones

(Readers may need to enable JavaScript from ‘’ to be able to view the work-sheet below: )

I suppose that there’s an observation I should add. Using just a matrix of unit eigenvectors has as caveat, a possible outcome in which the eigenvectors are still not orthogonal. If that’s the case, then to use the transpose in place of the inverse is not acceptable.

If the reader is familiar with the exercise which I linked to at the top of this posting, he or she will notice that the matrix which I’m diagonalizing is diagonally symmetrical. This is because coefficients belonging to the quadric it represents, have either been given to one diagonal element, or distributed between two elements of the matrix equally.

In that case, the matrix of eigenvectors will be orthogonal.



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